Dynamic Constrained Multi-Objective Evolutionary Optimization via Adaptive Two-Stage Archiving and Autoencoder Prediction
Qianhui Wang, Yulong Ye, Qingling Zhu, Songbai Liu, Qiuzhen Lin · 2023
Dynamic constrained multi-objective optimization problems (DCMOPs) are characterized by time-varying objectives and constraints, requiring optimization algorithms that can rapidly track the changing Pareto-Optimal Set (POS).A new dynamic constrained multi-objective evolutionary algorithm with adaptive two-stage archiving and autoencoder prediction is proposed in this paper, called ATAP to effectively solve DCMOPs. Specifically, ATAP designs a differential denoising autoencoder (DDA) prediction strategy, which applies a denoising autoencoder to thoroughly analyze change trends of historical population and predict some initial solutions in the new environment. Subsequently, to promote greater diversity within the initial population, a differential rule is implemented, effectively addressing the potential scarcity of diversity caused by constraints. Moreover, ATAP introduces an adaptive two-stage archiving (ATA) constraint handling technique, which can dynamically adjust evolutionary stages based on the state of the population. This approach can adaptively determine preferences between objectives and constraints, achieving a better balance. In this way, ATA and DDA are well cooperated to efficiently solve DCMOPs with time-varying constraints and objectives. The experimental results demonstrate that the proposed ATAP is effective and has some advantages over three competitive algorithms when solving the CEC2023 DCMOP benchmark.